Adaptive Authentication via Relational and Sentiment Analysis
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Solution Overview
Problem
Existing authentication methods, particularly in financial institutions, face challenges in providing secure access while minimizing user dissatisfaction and fraud risks, especially when users access accounts from unfamiliar devices or locations, as they often rely on static credentials and are vulnerable to malware intercepts.
Innovation Solution
A system that infers the expected user context by analyzing social network data and comparing it with real-time activity, dynamically adjusting authentication levels based on relational and sentiment analysis to ensure secure access, allowing users to adjust security settings and mitigating risks through adaptive authentication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static credentials and traditional authentication methods are used, then ease of operation is improved, but security and vulnerability to fraud increase
Solution Approach 1:
The patent implements dynamic authentication by transitioning from static credentials to context-aware authentication levels. The system continuously monitors user context (location, device, behavior patterns) and dynamically adjusts authentication requirements in real-time. When context indicates high risk, additional authentication steps are triggered; when context is trusted, authentication is streamlined, resolving the contradiction between ease of operation and security.
Solution Approach 2:
The system changes authentication parameters based on contextual factors. Instead of using a fixed authentication method, the system varies authentication parameters (such as requiring biometric verification, password confirmation, or device recognition) based on detected context parameters like location, time, device type, and user behavior patterns, thereby adapting security requirements to actual risk levels.
2Reliability
If multiple authentication steps are required for security, then security is improved, but user dissatisfaction and ease of operation worsen
Solution Approach 1:
The patent applies local quality by tailoring authentication requirements to specific contexts and situations. Rather than applying uniform authentication to all users and transactions, the system identifies local characteristics of each authentication event (such as familiar device, trusted location, low-risk transaction) and applies appropriate authentication depth accordingly, making authentication both secure and convenient where possible.
Solution Approach 2:
The system dynamically adjusts the number and type of authentication steps based on real-time risk assessment. When context indicates low risk (trusted device, familiar location), the system reduces authentication steps to minimize user friction. When context indicates high risk (new device, unusual location, high-value transaction), the system automatically increases authentication steps to maintain security, thus resolving the contradiction between security and ease of operation.
3Reliability
If authentication is performed from unfamiliar devices or locations, then security is improved, but ease of operation and user experience worsen
Solution Approach 1:
The system performs preliminary actions by establishing baseline user profiles and trusted contexts in advance through machine learning and behavioral analysis. By pre-learning user patterns, devices, and locations during legitimate use periods, the system can quickly identify and authenticate legitimate access even from new devices or locations without requiring excessive verification steps, thus maintaining ease of operation while preserving security.
Solution Approach 2:
The system implements feedback loops that continuously monitor authentication events and user behavior. When a user accesses from an unfamiliar device or location, the system provides contextual feedback (such as risk indicators or verification prompts) and learns from the user's response. This feedback mechanism allows the system to balance security requirements with user convenience by adapting to legitimate access patterns while detecting and preventing fraud.
Data Source
AI summary
Provided is adaptive authentication that utilizes relational analysis, sentiment analysis, or both relational analysis and sentiment analysis to facilitate an authentication procedure. The relational analysis evaluates a transactional profile and a behavioral profile of the user. The sentiment analysis evaluates available user information that is obtained from various forms of Internet activity related to the user. A level of authentication is selectively modified based on a result of the relational analysis and/or the sentiment analysis.